Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations10839
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory249.0 B

Variable types

Text1
Numeric16
Categorical2

Alerts

MRG has constant value "0" Constant
ARPU_SEGMENT is highly overall correlated with FREQUENCE and 6 other fieldsHigh correlation
FREQUENCE is highly overall correlated with ARPU_SEGMENT and 4 other fieldsHigh correlation
FREQUENCE_RECH is highly overall correlated with ARPU_SEGMENT and 4 other fieldsHigh correlation
FREQ_TOP_PACK is highly overall correlated with ARPU_SEGMENT and 4 other fieldsHigh correlation
MONTANT is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
ON_NET is highly overall correlated with ORANGEHigh correlation
ORANGE is highly overall correlated with ARPU_SEGMENT and 4 other fieldsHigh correlation
REGULARITY is highly overall correlated with ARPU_SEGMENT and 2 other fieldsHigh correlation
REVENUE is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
TIGO is highly overall correlated with ORANGEHigh correlation
CHURN is highly imbalanced (98.4%) Imbalance
user_id has unique values Unique
REGION has 6731 (62.1%) zeros Zeros
DATA_VOLUME has 2260 (20.9%) zeros Zeros
ON_NET has 203 (1.9%) zeros Zeros
TIGO has 775 (7.2%) zeros Zeros
ZONE1 has 3558 (32.8%) zeros Zeros
ZONE2 has 3141 (29.0%) zeros Zeros

Reproduction

Analysis started2025-04-19 13:46:50.057794
Analysis finished2025-04-19 13:47:05.097324
Duration15.04 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

user_id
Text

Unique 

Distinct10839
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-04-19T16:47:05.229586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters433560
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10839 ?
Unique (%)100.0%

Sample

1st row00000bfd7d50f01092811bc0c8d7b0d6fe7c3596
2nd row000134b94b87f70dc30597aa19598fbeaa70dc7f
3rd row0004f3a6671684d984c83c2cfa30647665dd430b
4th row001191f7d3aec518be9fcd550015fb45826c3ed2
5th row0013b5ca16d87ad1d286fe66d46754b80b9e22fc
ValueCountFrequency (%)
003d653c4f398826e80d4c00934e8b494c35d0fb 1
 
< 0.1%
fff8b64302bda7f59ff196af759e1de7c4215f6d 1
 
< 0.1%
00000bfd7d50f01092811bc0c8d7b0d6fe7c3596 1
 
< 0.1%
000134b94b87f70dc30597aa19598fbeaa70dc7f 1
 
< 0.1%
0004f3a6671684d984c83c2cfa30647665dd430b 1
 
< 0.1%
001191f7d3aec518be9fcd550015fb45826c3ed2 1
 
< 0.1%
0013b5ca16d87ad1d286fe66d46754b80b9e22fc 1
 
< 0.1%
ff94c0dc594556f211bba78587c20ad8c278128b 1
 
< 0.1%
ffa95fe39c8d39e420d5a48e2fceb635b28bca86 1
 
< 0.1%
ffab6191020103260404e50115f1cf3d0b0a4390 1
 
< 0.1%
Other values (10829) 10829
99.9%
2025-04-19T16:47:05.379423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 27416
 
6.3%
0 27349
 
6.3%
3 27327
 
6.3%
8 27265
 
6.3%
c 27226
 
6.3%
e 27195
 
6.3%
b 27152
 
6.3%
4 27129
 
6.3%
f 27049
 
6.2%
a 27044
 
6.2%
Other values (6) 161408
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 433560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 27416
 
6.3%
0 27349
 
6.3%
3 27327
 
6.3%
8 27265
 
6.3%
c 27226
 
6.3%
e 27195
 
6.3%
b 27152
 
6.3%
4 27129
 
6.3%
f 27049
 
6.2%
a 27044
 
6.2%
Other values (6) 161408
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 433560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 27416
 
6.3%
0 27349
 
6.3%
3 27327
 
6.3%
8 27265
 
6.3%
c 27226
 
6.3%
e 27195
 
6.3%
b 27152
 
6.3%
4 27129
 
6.3%
f 27049
 
6.2%
a 27044
 
6.2%
Other values (6) 161408
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 433560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 27416
 
6.3%
0 27349
 
6.3%
3 27327
 
6.3%
8 27265
 
6.3%
c 27226
 
6.3%
e 27195
 
6.3%
b 27152
 
6.3%
4 27129
 
6.3%
f 27049
 
6.2%
a 27044
 
6.2%
Other values (6) 161408
37.2%

REGION
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1235354
Minimum0
Maximum13
Zeros6731
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:05.427675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.5633025
Coefficient of variation (CV)1.4609415
Kurtosis-0.590438
Mean3.1235354
Median Absolute Deviation (MAD)0
Skewness1.0379363
Sum33856
Variance20.82373
MonotonicityNot monotonic
2025-04-19T16:47:05.467955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 6731
62.1%
12 965
 
8.9%
9 768
 
7.1%
4 577
 
5.3%
11 378
 
3.5%
6 327
 
3.0%
7 280
 
2.6%
13 220
 
2.0%
1 167
 
1.5%
2 138
 
1.3%
Other values (4) 288
 
2.7%
ValueCountFrequency (%)
0 6731
62.1%
1 167
 
1.5%
2 138
 
1.3%
3 136
 
1.3%
4 577
 
5.3%
5 5
 
< 0.1%
6 327
 
3.0%
7 280
 
2.6%
8 115
 
1.1%
9 768
 
7.1%
ValueCountFrequency (%)
13 220
 
2.0%
12 965
8.9%
11 378
 
3.5%
10 32
 
0.3%
9 768
7.1%
8 115
 
1.1%
7 280
 
2.6%
6 327
 
3.0%
5 5
 
< 0.1%
4 577
5.3%

TENURE
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8721284
Minimum0
Maximum7
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:05.509371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q17
median7
Q37
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.64484914
Coefficient of variation (CV)0.093835432
Kurtosis35.183921
Mean6.8721284
Median Absolute Deviation (MAD)0
Skewness-5.7262475
Sum74487
Variance0.41583041
MonotonicityNot monotonic
2025-04-19T16:47:05.544757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 10341
95.4%
5 203
 
1.9%
4 101
 
0.9%
3 66
 
0.6%
6 60
 
0.6%
2 57
 
0.5%
1 9
 
0.1%
0 2
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 9
 
0.1%
2 57
 
0.5%
3 66
 
0.6%
4 101
 
0.9%
5 203
 
1.9%
6 60
 
0.6%
7 10341
95.4%
ValueCountFrequency (%)
7 10341
95.4%
6 60
 
0.6%
5 203
 
1.9%
4 101
 
0.9%
3 66
 
0.6%
2 57
 
0.5%
1 9
 
0.1%
0 2
 
< 0.1%

MONTANT
Real number (ℝ)

High correlation 

Distinct1224
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16442.896
Minimum200
Maximum235000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:05.589494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile3100
Q17800
median13000
Q320900
95-th percentile41005
Maximum235000
Range234800
Interquartile range (IQR)13100

Descriptive statistics

Standard deviation13816.041
Coefficient of variation (CV)0.84024378
Kurtosis28.209639
Mean16442.896
Median Absolute Deviation (MAD)6050
Skewness3.5236476
Sum1.7822455 × 108
Variance1.9088299 × 108
MonotonicityNot monotonic
2025-04-19T16:47:05.644735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 107
 
1.0%
9500 106
 
1.0%
12000 103
 
1.0%
13000 101
 
0.9%
5500 100
 
0.9%
15000 98
 
0.9%
10500 95
 
0.9%
5000 94
 
0.9%
7000 93
 
0.9%
12500 91
 
0.8%
Other values (1214) 9851
90.9%
ValueCountFrequency (%)
200 1
 
< 0.1%
400 1
 
< 0.1%
500 11
0.1%
550 1
 
< 0.1%
600 4
 
< 0.1%
700 7
 
0.1%
800 2
 
< 0.1%
850 2
 
< 0.1%
900 5
 
< 0.1%
1000 24
0.2%
ValueCountFrequency (%)
235000 1
< 0.1%
215000 1
< 0.1%
214000 1
< 0.1%
198000 1
< 0.1%
194800 1
< 0.1%
161000 1
< 0.1%
156746 1
< 0.1%
141000 1
< 0.1%
135500 1
< 0.1%
133000 1
< 0.1%

FREQUENCE_RECH
Real number (ℝ)

High correlation 

Distinct106
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.111818
Minimum1
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:05.694581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q112
median23
Q338
95-th percentile65
Maximum131
Range130
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.025954
Coefficient of variation (CV)0.7017587
Kurtosis0.7822502
Mean27.111818
Median Absolute Deviation (MAD)12
Skewness1.0214575
Sum293865
Variance361.98694
MonotonicityNot monotonic
2025-04-19T16:47:05.749326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 293
 
2.7%
5 285
 
2.6%
10 281
 
2.6%
16 280
 
2.6%
12 278
 
2.6%
15 273
 
2.5%
18 270
 
2.5%
13 267
 
2.5%
14 264
 
2.4%
9 259
 
2.4%
Other values (96) 8089
74.6%
ValueCountFrequency (%)
1 48
 
0.4%
2 114
 
1.1%
3 216
2.0%
4 231
2.1%
5 285
2.6%
6 293
2.7%
7 252
2.3%
8 246
2.3%
9 259
2.4%
10 281
2.6%
ValueCountFrequency (%)
131 1
 
< 0.1%
113 1
 
< 0.1%
112 1
 
< 0.1%
109 1
 
< 0.1%
103 1
 
< 0.1%
101 1
 
< 0.1%
100 3
< 0.1%
99 3
< 0.1%
98 2
< 0.1%
97 2
< 0.1%

REVENUE
Real number (ℝ)

High correlation 

Distinct7753
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16732.591
Minimum198
Maximum226150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:05.804416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile3244.1
Q17998
median13352
Q321217
95-th percentile41512.9
Maximum226150
Range225952
Interquartile range (IQR)13219

Descriptive statistics

Standard deviation13975.664
Coefficient of variation (CV)0.83523607
Kurtosis24.88136
Mean16732.591
Median Absolute Deviation (MAD)6248
Skewness3.3588567
Sum1.8136456 × 108
Variance1.9531918 × 108
MonotonicityNot monotonic
2025-04-19T16:47:05.853112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000 19
 
0.2%
6500 17
 
0.2%
4000 17
 
0.2%
5500 17
 
0.2%
11000 17
 
0.2%
9500 16
 
0.1%
8000 15
 
0.1%
10000 15
 
0.1%
7000 15
 
0.1%
4500 15
 
0.1%
Other values (7743) 10676
98.5%
ValueCountFrequency (%)
198 1
< 0.1%
249 1
< 0.1%
301 1
< 0.1%
314 1
< 0.1%
494 1
< 0.1%
500 2
< 0.1%
501 1
< 0.1%
509 1
< 0.1%
510 1
< 0.1%
511 1
< 0.1%
ValueCountFrequency (%)
226150 1
< 0.1%
221999 1
< 0.1%
221695 1
< 0.1%
168738 1
< 0.1%
161749 1
< 0.1%
159640 1
< 0.1%
137318 1
< 0.1%
136500 1
< 0.1%
135189 1
< 0.1%
129082 1
< 0.1%

ARPU_SEGMENT
Real number (ℝ)

High correlation 

Distinct5809
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5577.5322
Minimum66
Maximum75383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:06.004430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile1081.7
Q12666
median4451
Q37072.5
95-th percentile13837.3
Maximum75383
Range75317
Interquartile range (IQR)4406.5

Descriptive statistics

Standard deviation4658.5543
Coefficient of variation (CV)0.83523575
Kurtosis24.881254
Mean5577.5322
Median Absolute Deviation (MAD)2083
Skewness3.3588545
Sum60454871
Variance21702128
MonotonicityNot monotonic
2025-04-19T16:47:06.059440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1833 33
 
0.3%
4000 32
 
0.3%
1000 31
 
0.3%
1500 31
 
0.3%
2000 28
 
0.3%
2667 26
 
0.2%
2333 26
 
0.2%
3000 25
 
0.2%
3833 24
 
0.2%
4833 24
 
0.2%
Other values (5799) 10559
97.4%
ValueCountFrequency (%)
66 1
 
< 0.1%
83 1
 
< 0.1%
100 1
 
< 0.1%
105 1
 
< 0.1%
165 1
 
< 0.1%
167 3
< 0.1%
170 3
< 0.1%
171 1
 
< 0.1%
183 1
 
< 0.1%
200 1
 
< 0.1%
ValueCountFrequency (%)
75383 1
< 0.1%
74000 1
< 0.1%
73898 1
< 0.1%
56246 1
< 0.1%
53916 1
< 0.1%
53213 1
< 0.1%
45773 1
< 0.1%
45500 1
< 0.1%
45063 1
< 0.1%
43027 1
< 0.1%

FREQUENCE
Real number (ℝ)

High correlation 

Distinct91
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.330012
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:06.119557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median30
Q345
95-th percentile70
Maximum91
Range90
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.076706
Coefficient of variation (CV)0.5723582
Kurtosis-0.10624188
Mean33.330012
Median Absolute Deviation (MAD)13
Skewness0.6914411
Sum361264
Variance363.9207
MonotonicityNot monotonic
2025-04-19T16:47:06.169716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 251
 
2.3%
18 250
 
2.3%
23 250
 
2.3%
21 248
 
2.3%
31 243
 
2.2%
34 235
 
2.2%
13 232
 
2.1%
24 231
 
2.1%
28 228
 
2.1%
19 228
 
2.1%
Other values (81) 8443
77.9%
ValueCountFrequency (%)
1 6
 
0.1%
2 16
 
0.1%
3 47
 
0.4%
4 61
 
0.6%
5 93
0.9%
6 106
1.0%
7 146
1.3%
8 150
1.4%
9 198
1.8%
10 181
1.7%
ValueCountFrequency (%)
91 14
0.1%
90 10
 
0.1%
89 11
 
0.1%
88 18
0.2%
87 20
0.2%
86 20
0.2%
85 17
0.2%
84 19
0.2%
83 23
0.2%
82 29
0.3%

DATA_VOLUME
Real number (ℝ)

Zeros 

Distinct6139
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7301.9184
Minimum0
Maximum201413
Zeros2260
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:06.224417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median2082
Q39562
95-th percentile30642.8
Maximum201413
Range201413
Interquartile range (IQR)9558

Descriptive statistics

Standard deviation12869.652
Coefficient of variation (CV)1.7625028
Kurtosis30.104045
Mean7301.9184
Median Absolute Deviation (MAD)2082
Skewness4.2050909
Sum79145493
Variance1.6562793 × 108
MonotonicityNot monotonic
2025-04-19T16:47:06.284754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2260
 
20.9%
1 292
 
2.7%
2 103
 
1.0%
3 42
 
0.4%
4 36
 
0.3%
5 32
 
0.3%
6 26
 
0.2%
7 19
 
0.2%
11 17
 
0.2%
20 15
 
0.1%
Other values (6129) 7997
73.8%
ValueCountFrequency (%)
0 2260
20.9%
1 292
 
2.7%
2 103
 
1.0%
3 42
 
0.4%
4 36
 
0.3%
5 32
 
0.3%
6 26
 
0.2%
7 19
 
0.2%
8 9
 
0.1%
9 15
 
0.1%
ValueCountFrequency (%)
201413 1
< 0.1%
182985 1
< 0.1%
169758 1
< 0.1%
165225 1
< 0.1%
157418 1
< 0.1%
144532 1
< 0.1%
140012 1
< 0.1%
126272 1
< 0.1%
125018 1
< 0.1%
122333 1
< 0.1%

ON_NET
Real number (ℝ)

High correlation  Zeros 

Distinct1845
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean432.54784
Minimum0
Maximum19950
Zeros203
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:06.349350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q119
median88
Q3354
95-th percentile1884.3
Maximum19950
Range19950
Interquartile range (IQR)335

Descriptive statistics

Standard deviation1117.1308
Coefficient of variation (CV)2.5826757
Kurtosis83.73012
Mean432.54784
Median Absolute Deviation (MAD)82
Skewness7.4380319
Sum4688386
Variance1247981.2
MonotonicityNot monotonic
2025-04-19T16:47:06.404678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 251
 
2.3%
2 236
 
2.2%
0 203
 
1.9%
3 194
 
1.8%
4 181
 
1.7%
5 159
 
1.5%
6 147
 
1.4%
8 147
 
1.4%
7 136
 
1.3%
9 122
 
1.1%
Other values (1835) 9063
83.6%
ValueCountFrequency (%)
0 203
1.9%
1 251
2.3%
2 236
2.2%
3 194
1.8%
4 181
1.7%
5 159
1.5%
6 147
1.4%
7 136
1.3%
8 147
1.4%
9 122
1.1%
ValueCountFrequency (%)
19950 1
< 0.1%
19832 1
< 0.1%
19820 1
< 0.1%
18287 1
< 0.1%
17533 1
< 0.1%
17121 1
< 0.1%
17016 1
< 0.1%
16973 1
< 0.1%
16841 1
< 0.1%
15292 1
< 0.1%

ORANGE
Real number (ℝ)

High correlation 

Distinct1183
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234.34736
Minimum0
Maximum5543
Zeros64
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:06.459439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q138
median116
Q3296
95-th percentile826
Maximum5543
Range5543
Interquartile range (IQR)258

Descriptive statistics

Standard deviation348.50146
Coefficient of variation (CV)1.487115
Kurtosis30.715644
Mean234.34736
Median Absolute Deviation (MAD)95
Skewness4.3299714
Sum2540091
Variance121453.27
MonotonicityNot monotonic
2025-04-19T16:47:06.514678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 117
 
1.1%
3 103
 
1.0%
2 96
 
0.9%
7 92
 
0.8%
5 86
 
0.8%
8 86
 
0.8%
14 85
 
0.8%
12 82
 
0.8%
10 80
 
0.7%
4 79
 
0.7%
Other values (1173) 9933
91.6%
ValueCountFrequency (%)
0 64
0.6%
1 117
1.1%
2 96
0.9%
3 103
1.0%
4 79
0.7%
5 86
0.8%
6 72
0.7%
7 92
0.8%
8 86
0.8%
9 77
0.7%
ValueCountFrequency (%)
5543 1
< 0.1%
4877 1
< 0.1%
4107 1
< 0.1%
3965 1
< 0.1%
3953 1
< 0.1%
3939 1
< 0.1%
3771 1
< 0.1%
3637 1
< 0.1%
3605 1
< 0.1%
3558 1
< 0.1%

TIGO
Real number (ℝ)

High correlation  Zeros 

Distinct422
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.49368
Minimum0
Maximum2144
Zeros775
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:06.569870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median11
Q335
95-th percentile156
Maximum2144
Range2144
Interquartile range (IQR)32

Descriptive statistics

Standard deviation86.058411
Coefficient of variation (CV)2.358173
Kurtosis106.14089
Mean36.49368
Median Absolute Deviation (MAD)10
Skewness8.088495
Sum395555
Variance7406.0501
MonotonicityNot monotonic
2025-04-19T16:47:06.624379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1033
 
9.5%
0 775
 
7.2%
2 732
 
6.8%
3 557
 
5.1%
4 423
 
3.9%
5 394
 
3.6%
6 356
 
3.3%
7 303
 
2.8%
8 290
 
2.7%
9 232
 
2.1%
Other values (412) 5744
53.0%
ValueCountFrequency (%)
0 775
7.2%
1 1033
9.5%
2 732
6.8%
3 557
5.1%
4 423
3.9%
5 394
 
3.6%
6 356
 
3.3%
7 303
 
2.8%
8 290
 
2.7%
9 232
 
2.1%
ValueCountFrequency (%)
2144 1
< 0.1%
1613 1
< 0.1%
1471 1
< 0.1%
1456 1
< 0.1%
1306 1
< 0.1%
1302 1
< 0.1%
1289 1
< 0.1%
1275 1
< 0.1%
1249 1
< 0.1%
1109 1
< 0.1%

ZONE1
Real number (ℝ)

Zeros 

Distinct240
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8772027
Minimum0
Maximum1609
Zeros3558
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:06.674562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile35
Maximum1609
Range1609
Interquartile range (IQR)4

Descriptive statistics

Standard deviation49.879415
Coefficient of variation (CV)5.0499536
Kurtosis316.97404
Mean9.8772027
Median Absolute Deviation (MAD)1
Skewness15.013475
Sum107059
Variance2487.956
MonotonicityNot monotonic
2025-04-19T16:47:06.734752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3558
32.8%
1 2400
22.1%
2 1243
 
11.5%
3 643
 
5.9%
4 465
 
4.3%
5 335
 
3.1%
6 238
 
2.2%
7 187
 
1.7%
8 159
 
1.5%
9 144
 
1.3%
Other values (230) 1467
13.5%
ValueCountFrequency (%)
0 3558
32.8%
1 2400
22.1%
2 1243
 
11.5%
3 643
 
5.9%
4 465
 
4.3%
5 335
 
3.1%
6 238
 
2.2%
7 187
 
1.7%
8 159
 
1.5%
9 144
 
1.3%
ValueCountFrequency (%)
1609 1
< 0.1%
1427 1
< 0.1%
1206 1
< 0.1%
1204 1
< 0.1%
1133 1
< 0.1%
900 1
< 0.1%
883 1
< 0.1%
859 1
< 0.1%
793 1
< 0.1%
772 1
< 0.1%

ZONE2
Real number (ℝ)

Zeros 

Distinct199
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2854507
Minimum0
Maximum1324
Zeros3141
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:06.789302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile42
Maximum1324
Range1324
Interquartile range (IQR)6

Descriptive statistics

Standard deviation29.901377
Coefficient of variation (CV)3.2202397
Kurtosis443.69882
Mean9.2854507
Median Absolute Deviation (MAD)2
Skewness14.931957
Sum100645
Variance894.09234
MonotonicityNot monotonic
2025-04-19T16:47:06.844311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3141
29.0%
1 2002
18.5%
2 1050
 
9.7%
3 748
 
6.9%
4 527
 
4.9%
5 388
 
3.6%
6 296
 
2.7%
7 254
 
2.3%
8 167
 
1.5%
11 145
 
1.3%
Other values (189) 2121
19.6%
ValueCountFrequency (%)
0 3141
29.0%
1 2002
18.5%
2 1050
 
9.7%
3 748
 
6.9%
4 527
 
4.9%
5 388
 
3.6%
6 296
 
2.7%
7 254
 
2.3%
8 167
 
1.5%
9 143
 
1.3%
ValueCountFrequency (%)
1324 1
< 0.1%
702 1
< 0.1%
641 1
< 0.1%
585 1
< 0.1%
516 1
< 0.1%
503 1
< 0.1%
407 1
< 0.1%
396 1
< 0.1%
394 1
< 0.1%
383 1
< 0.1%

MRG
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size698.6 KiB
0
10839 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10839
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10839
100.0%

Length

2025-04-19T16:47:06.989363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T16:47:07.019461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10839
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10839
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10839
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10839
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10839
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10839
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10839
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10839
100.0%

REGULARITY
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.306394
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:07.054619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q152
median60
Q362
95-th percentile62
Maximum62
Range61
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.06016
Coefficient of variation (CV)0.18189868
Kurtosis3.8472636
Mean55.306394
Median Absolute Deviation (MAD)2
Skewness-1.9850036
Sum599466
Variance101.20682
MonotonicityNot monotonic
2025-04-19T16:47:07.102394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 4080
37.6%
61 1091
 
10.1%
60 652
 
6.0%
59 532
 
4.9%
58 404
 
3.7%
57 351
 
3.2%
56 301
 
2.8%
55 254
 
2.3%
54 228
 
2.1%
53 227
 
2.1%
Other values (50) 2719
25.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 3
 
< 0.1%
9 4
< 0.1%
10 6
0.1%
11 8
0.1%
12 5
< 0.1%
ValueCountFrequency (%)
62 4080
37.6%
61 1091
 
10.1%
60 652
 
6.0%
59 532
 
4.9%
58 404
 
3.7%
57 351
 
3.2%
56 301
 
2.8%
55 254
 
2.3%
54 228
 
2.1%
53 227
 
2.1%

TOP_PACK
Real number (ℝ)

Distinct68
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.224375
Minimum0
Maximum67
Zeros26
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:07.159483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q17
median17
Q337
95-th percentile56
Maximum67
Range67
Interquartile range (IQR)30

Descriptive statistics

Standard deviation18.067956
Coefficient of variation (CV)0.77797383
Kurtosis-0.73913925
Mean23.224375
Median Absolute Deviation (MAD)10
Skewness0.76150373
Sum251729
Variance326.45104
MonotonicityNot monotonic
2025-04-19T16:47:07.213824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 3013
27.8%
21 1110
 
10.2%
10 795
 
7.3%
51 779
 
7.2%
47 560
 
5.2%
37 546
 
5.0%
11 541
 
5.0%
29 482
 
4.4%
44 478
 
4.4%
13 357
 
3.3%
Other values (58) 2178
20.1%
ValueCountFrequency (%)
0 26
 
0.2%
1 142
 
1.3%
2 17
 
0.2%
3 6
 
0.1%
4 3
 
< 0.1%
5 212
 
2.0%
6 22
 
0.2%
7 3013
27.8%
8 85
 
0.8%
9 2
 
< 0.1%
ValueCountFrequency (%)
67 14
 
0.1%
66 211
1.9%
65 69
 
0.6%
64 3
 
< 0.1%
63 123
1.1%
62 3
 
< 0.1%
61 4
 
< 0.1%
60 1
 
< 0.1%
59 11
 
0.1%
58 10
 
0.1%

FREQ_TOP_PACK
Real number (ℝ)

High correlation 

Distinct138
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.780054
Minimum1
Maximum560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.4 KiB
2025-04-19T16:47:07.260614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median13
Q325
95-th percentile56
Maximum560
Range559
Interquartile range (IQR)19

Descriptive statistics

Standard deviation20.025398
Coefficient of variation (CV)1.0663121
Kurtosis68.950022
Mean18.780054
Median Absolute Deviation (MAD)8
Skewness4.6204338
Sum203557
Variance401.01658
MonotonicityNot monotonic
2025-04-19T16:47:07.319439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 643
 
5.9%
2 548
 
5.1%
4 499
 
4.6%
5 478
 
4.4%
1 428
 
3.9%
6 427
 
3.9%
7 426
 
3.9%
9 418
 
3.9%
8 391
 
3.6%
10 367
 
3.4%
Other values (128) 6214
57.3%
ValueCountFrequency (%)
1 428
3.9%
2 548
5.1%
3 643
5.9%
4 499
4.6%
5 478
4.4%
6 427
3.9%
7 426
3.9%
8 391
3.6%
9 418
3.9%
10 367
3.4%
ValueCountFrequency (%)
560 1
< 0.1%
350 1
< 0.1%
286 1
< 0.1%
258 1
< 0.1%
257 1
< 0.1%
214 1
< 0.1%
206 1
< 0.1%
198 1
< 0.1%
193 1
< 0.1%
174 1
< 0.1%

CHURN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size698.6 KiB
0
10823 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10839
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10823
99.9%
1 16
 
0.1%

Length

2025-04-19T16:47:07.371610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T16:47:07.399483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10823
99.9%
1 16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 10823
99.9%
1 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10839
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10823
99.9%
1 16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10839
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10823
99.9%
1 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10839
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10823
99.9%
1 16
 
0.1%

Interactions

2025-04-19T16:47:04.066245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:50.543205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:51.525228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:52.379945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:53.254824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:54.111850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:54.939638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:55.899444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:56.699628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:57.704758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:58.634661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:59.449686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:00.374694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:01.289279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:02.234624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:03.093385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:04.125549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:50.604637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:51.574377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:52.427620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:53.299807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:54.176098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:54.997397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:55.945051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:56.754593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:57.764058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:58.679472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:59.499583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:00.429451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:01.339592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:02.293697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:03.149629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:04.179342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:50.659454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:51.629117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:52.479861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:53.361513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:54.229768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:55.049339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:55.999918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:56.814188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:57.819429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:58.735523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-19T16:46:54.064490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:54.884544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:55.845424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:56.649880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:57.646123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:58.578509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:46:59.399522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:00.329714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:01.229460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:02.184239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:03.043679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-19T16:47:03.904465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-19T16:47:07.434767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ARPU_SEGMENTCHURNDATA_VOLUMEFREQUENCEFREQUENCE_RECHFREQ_TOP_PACKMONTANTON_NETORANGEREGIONREGULARITYREVENUETENURETIGOTOP_PACKZONE1ZONE2
ARPU_SEGMENT1.0000.0000.2810.7120.6850.6080.9780.3430.549-0.1720.5321.0000.0100.411-0.0660.2110.302
CHURN0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.0000.0110.0000.0000.000
DATA_VOLUME0.2810.0001.0000.2230.1710.1120.268-0.190-0.089-0.2570.2870.281-0.042-0.0280.059-0.0810.026
FREQUENCE0.7120.0000.2231.0000.9140.7780.7090.1550.239-0.1220.4460.712-0.0250.1930.0210.0280.216
FREQUENCE_RECH0.6850.0000.1710.9141.0000.8060.6850.1770.240-0.0960.3960.685-0.0230.195-0.0640.0260.203
FREQ_TOP_PACK0.6080.0000.1120.7780.8061.0000.5990.2430.338-0.0420.3850.608-0.0100.247-0.0620.0400.058
MONTANT0.9780.0000.2680.7090.6850.5991.0000.3440.547-0.1650.5300.9780.0100.410-0.0630.2090.302
ON_NET0.3430.000-0.1900.1550.1770.2430.3441.0000.5940.1560.3710.3430.0570.4310.1780.123-0.088
ORANGE0.5490.000-0.0890.2390.2400.3380.5470.5941.000-0.0110.3610.5490.0550.557-0.0080.165-0.015
REGION-0.1720.000-0.257-0.122-0.096-0.042-0.1650.156-0.0111.000-0.029-0.172-0.008-0.0400.058-0.052-0.153
REGULARITY0.5320.1070.2870.4460.3960.3850.5300.3710.361-0.0291.0000.5320.0220.2880.0570.0700.038
REVENUE1.0000.0000.2810.7120.6850.6080.9780.3430.549-0.1720.5321.0000.0100.411-0.0660.2110.302
TENURE0.0100.000-0.042-0.025-0.023-0.0100.0100.0570.055-0.0080.0220.0101.0000.0450.0040.026-0.003
TIGO0.4110.011-0.0280.1930.1950.2470.4100.4310.557-0.0400.2880.4110.0451.000-0.0180.1040.007
TOP_PACK-0.0660.0000.0590.021-0.064-0.062-0.0630.178-0.0080.0580.057-0.0660.004-0.0181.000-0.127-0.048
ZONE10.2110.000-0.0810.0280.0260.0400.2090.1230.165-0.0520.0700.2110.0260.104-0.1271.0000.079
ZONE20.3020.0000.0260.2160.2030.0580.302-0.088-0.015-0.1530.0380.302-0.0030.007-0.0480.0791.000

Missing values

2025-04-19T16:47:04.929425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-19T16:47:05.009769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
000000bfd7d50f01092811bc0c8d7b0d6fe7c3596274250.015.04251.01417.017.04.0388.046.01.01.02.0054518.00
37000134b94b87f70dc30597aa19598fbeaa70dc7f0710400.024.010900.03633.026.011815.012.059.03.00.01.0054218.00
1700004f3a6671684d984c83c2cfa30647665dd430b073000.06.02999.01000.07.0515.015.00.00.00.00.0058215.00
561001191f7d3aec518be9fcd550015fb45826c3ed21272750.06.02748.0916.06.00.033.01.022.00.08.0039471.00
6350013b5ca16d87ad1d286fe66d46754b80b9e22fc0711700.021.012698.04233.033.02.0170.0249.015.06.03.0062717.00
123100254fdf431af60f3eaa471b6fd9347b85625e260718000.041.018293.06098.055.030438.0291.0267.08.04.06.00611733.00
1254002608756e211251f6ad3b8d0c7d17760db171e21278700.04.010203.03401.014.015248.01318.0354.027.00.00.0062373.00
2026003d653c4f398826e80d4c00934e8b494c35d0fb472900.013.03562.01187.020.00.0269.059.025.01.00.00525114.00
2496004beb0fb6ab1bb7f01295c892f6deac3c6116090736250.069.037551.012517.083.05977.080.035.022.00.032.00612958.00
2618004fa2506a51129955773d197c43f36642b6b5290718650.040.018588.06196.045.06776.010.041.01.05.032.00592920.00
user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
2151840ffbc92b18c368927ef3cbf94b8bfaa9d8933f8d30714650.032.014470.04823.039.027089.066.032.01.09.01.00541711.00
2151920ffbecdb24391bd0bb8e42eaa1f5bdd328083e2039556700.077.057399.019133.066.00.0596.01300.032.015.022.0062796.00
2152085ffc34a4c7b0e5d3bff7e7bb1932fcc1071d16dbc9714850.047.015950.05317.051.03827.07270.0409.01.00.03.00621026.00
2152224ffc7cc878f857dcf0e8b62d42ebf314bbf9f8b4d0735000.029.035497.011832.031.01564.057.017.04.0304.04.0061129.00
2152322ffcb0039e1cee9535d70450f7f37d047151e6ebf0718700.039.019348.06449.048.010735.0206.0266.028.00.00.0062718.00
2152712ffd68ad289fd2d46347b7b48619671d532da12fa0712100.028.011661.03887.025.00.0204.0218.036.00.01.0057338.00
2153142ffe457524394dca68e4d8ce1b2a096682d1ae879853000.012.02989.0996.018.04590.03.048.06.00.00.0040176.00
2153483ffee32230918433bcbac7cd6d9004fe79ef950530712000.08.011539.03846.09.09179.01405.0384.042.00.02.0061373.00
2153547fff01392a3349a53a2936bf35ed25db7f11db14e0714600.027.015599.05200.029.00.032.0304.013.02.02.0052728.00
2153831fff8b64302bda7f59ff196af759e1de7c4215f6d0740000.05.023197.07732.018.0662.01143.0568.075.047.012.0061341.00